• Contribute to the design and execution of experiments in reasoning model training, post-training alignment, test-time scaling, or systematic model evaluation — with a focus on applications in financial and crypto-native contexts • Review and synthesize recent academic literature at NeurIPS, ICML, ICLR, and ACL — tracking developments in reasoning, alignment, and agentic AI to inform and sharpen ongoing research directions • Implement model variants, training procedures including RLVR-based approaches, and evaluation protocols using PyTorch and the Hugging Face ecosystem • Track and log experiments systematically using Weights & Biases or equivalent — maintaining reproducibility standards throughout • Explore the intersection of LLM reasoning and crypto-native data: on-chain signals, market microstructure, multi-modal market intelligence — identifying research opportunities unique to Binance's position • Collaborate with applied engineering teams to understand how research findings translate into production constraints in a zero-downtime, 24/7 trading environment

At Netflix, our mission is to entertain the world. Together, we are writing the next episode - pushing the boundaries of storytelling, global fandom and making the unimaginable a reality. We are a dream team obsessed with the uncomfortable excitement of discovering what happens when you merge creativity, intuition and cutting-edge technology. Come be a part of what’s next. The Ads Data Science & Engineering team is responsible for the foundational logic of the Netflix ads business. We develop the analyses, tools, and predictive algorithms that drive member joy and advertiser value. As a Data Scientist in this 0–1 space, you will not just execute tasks; you will be a primary architect of our experimentation roadmap, helping us navigate the transition from a nascent offering to a sophisticated global marketplace. Key Responsibilities - Scalable Experimentation & Frameworks: Design and execute rigorous experimental frameworks. You will lead the transition from manual analysis to automated, scalable solutions integrated with the Netflix Experimentation Platform, defining best practices that ensure trustworthy decision-making at scale. - Marketplace Dynamics & Auction Theory: Drive the advancement and implementation of biddable media models and dynamically priced auctions. You will develop strategies to optimize marketplace mechanics, ensuring balance between supply, demand, and member experience. - Global Expansion & Scaling: Partner with Product and Engineering to solve "cold-start" measurement challenges and rapidly scale our ad platform across diverse international markets. - Strategic Thought Leadership: Act as a high-level consultant to Product, Strategy, and Engineering teams. You will autonomously identify research opportunities, quantify their potential business impact, and advocate for resource allocation to pursue them. - Collaborative Partner: Cultivate strong partnerships with cross-functional stakeholders including product, engineering, operations, design, and consumer research. - Technical Excellence: Deliver end-to-end solutions using advanced causal inference, machine learning, and data exploration, maintaining a high bar for documentation and reproducibility. Requirements - Advanced Quantitative Background: MS or PhD in a quantitative field (e.g., Statistics, Economics, Mathematics) or equivalent practical experience. - Specialized Domain Expertise: Significant experience with auction dynamics, yield management, or supply-demand matching within a marketplace setting. - Statistical Rigor: Mastery of causal inference and experimental design, with specific experience solving for interference and network effects in marketplace environments. - Technical Stack: Expert proficiency in Python or R, and advanced SQL. - Strategic Communication: Ability to translate complex statistical results into actionable business narratives for stakeholders at all levels of the organization. - Netflix Culture: A self-starter who thrives in an environment of radical transparency and high autonomy. You are a mentor and a collaborator who prioritizes the inclusion of diverse perspectives to reach the best possible decisions. Generally, our compensation structure consists solely of an annual salary; we do not have bonuses. You choose each year how much of your compensation you want in salary versus stock options. To determine your personal top of market compensation, we rely on market indicators and consider your specific job family, background, skills, and experience to determine your compensation in the market range. The range for this role is $372,000.00 - $600,000.00. This compensation range will vary based on location. Netflix provides comprehensive benefits including Health Plans, Mental Health support, a 401(k) Retirement Plan with employer match, Stock Option Program, Disability Programs, Health Savings and Flexible Spending Accounts, Family-forming benefits, and Life and Serious Injury Benefits. We also offer paid leave of absence programs. Full-time hourly employees accrue 35 days annually for paid time off to be used for vacation, holidays, and sick paid time off. Full-time salaried employees are immediately entitled to flexible time off. See more details about our Benefits here. Netflix is a unique culture and environment. Learn more here. Inclusion is a Netflix value and we strive to host a meaningful interview experience for all candidates. If you want an accommodation/adjustment for a disability or any other reason during the hiring process, please send a request to your recruiting partner. We are an equal-opportunity employer and celebrate diversity, recognizing that diversity builds stronger teams. We approach diversity and inclusion seriously and thoughtfully. We do not discriminate on the basis of race, religion, color, ancestry, national origin, caste, sex, sexual orientation, gender, gender identity or expression, age, disability, medical condition, pregnancy, genetic makeup, marital status, or military service. Job is open for no less than 7 days and will be removed when the position is filled.

Binance is a leading global blockchain ecosystem behind the world’s largest cryptocurrency exchange by trading volume and registered users. We are trusted by over 230 million people in 100+ countries for our industry-leading security, user fund transparency, trading engine speed, deep liquidity, and an unmatched portfolio of digital-asset products. Binance offerings range from trading and finance to education, research, payments, institutional services, Web3 features, and more. We leverage the power of digital assets and blockchain to build an inclusive financial ecosystem to advance the freedom of money and improve financial access for people around the world. About the Pioneer Talent Program Are you an early-career professional ready to kickstart your career in the dynamic world of blockchain? Binance’s Pioneer Talent Program is designed to nurture ambitious individuals like you. The program offers full-time roles where participants will gain hands-on expertise via a tailored training curriculum and personalized 1:1 mentorship from industry leaders. Dive behind the scenes of the world’s leading blockchain ecosystem while building the skills to thrive in Web3. Launch your career with purpose—join us in shaping the future of digital innovation. Who May Apply Candidates with up to 5 years of relevant work experience About the Role We are seeking a Research Data Scientist who combines rigorous research thinking with a drive to see findings land in production. You will conduct original research in generative AI and LLMs applied to crypto-native problems — spanning reasoning under uncertainty, agent reliability in adversarial financial environments, and multi-modal market intelligence. This is a research-first role at the intersection of three critical frontiers: post-training alignment and reasoning for financial agents, efficient inference and test-time scaling, and multi-agent coordination in open, adversarial environments. The goal is real-world impact — not just papers, but agents that trade, analyze, and act for millions of users. You formulate research questions, design rigorous experiments, build evaluation frameworks, and collaborate with engineers to bring research into production. Responsibilities - Conduct original research in generative AI and large language models applied to crypto and financial domains — with focus areas including reasoning under market uncertainty, alignment of trading agents, multi-agent coordination, and test-time scaling for time-critical decisions - Design and execute rigorous experiments — formulating clear hypotheses, implementing model and training variants including RLVR-based reasoning approaches, running systematic ablations, and drawing statistically sound conclusions - Develop novel post-training methodologies and evaluation frameworks for LLMs operating in crypto contexts — covering chain-of-thought quality for market analysis, agent decision consistency, and robustness against adversarial prompt injection - Research test-time scaling techniques — process reward models, self-consistency, Monte Carlo Tree Search-based planning — and apply them to improve agent reasoning quality in ambiguous, fast-moving market conditions - Critically track developments across the research community at NeurIPS, ICML, ICLR, ACL, and crypto-adjacent venues — identifying high-leverage opportunities to apply state-of-the-art techniques to Binance's unique challenges Qualifications - Master's or PhD in Machine Learning, Computer Science, Mathematics, Statistics, or related field strongly preferred; exceptional Bachelor's candidates with demonstrable research output will be considered - 0–5 years of research or industry experience in ML/AI; strong academic lab or research internship experience equally valued - Deep understanding of transformer architectures, large language model pretraining dynamics, and post-training methodology — including the shift from RLHF toward RLVR-based reasoning model training - Proficiency in Python and PyTorch, Critically, you work in an AI-native way — using vibe coding practices with tools like Claude Code, Cursor, or Copilot Workspace as your primary development workflow - Rigorous mathematical foundations: linear algebra, probability theory, information theory, stochastic processes, and numerical optimization - Bilingual English/Mandarin is required to be able to coordinate with overseas partners and stakeholders Why Binance • Shape the future with the world’s leading blockchain ecosystem • Collaborate with world-class talent in a user-centric global organization with a flat structure • Tackle unique, fast-paced projects with autonomy in an innovative environment • Thrive in a results-driven workplace with opportunities for career growth and continuous learning • Competitive salary and company benefits • Work-from-home arrangement (the arrangement may vary depending on the work nature of the business team) Binance is committed to being an equal opportunity employer. We believe that having a diverse workforce is fundamental to our success. By submitting a job application, you confirm that you have read and agree to our Candidate Privacy Notice.

• Build and ship ML models that drive offer personalization, ranking, and targeting using transaction, merchant, and user behavioral data. • Develop features and training pipelines on top of large-scale event and transaction datasets (e.g., spend patterns, visit frequency, merchant affinity). • Design and analyze A/B tests and incrementality experiments to measure campaign and model impact. • Apply causal inference methods (e.g., matching, uplift modeling, diff-in-diff) to quantify partner ROI and user behavior changes. • Partner with ML and Data Engineers to productionize models, including feature stores, batch/real-time scoring, and monitoring. • Improve and iterate on ranking/recommendation systems to optimize engagement, conversion, and retention. • Contribute to attribution and measurement systems that help brands understand incremental value from Kard campaigns. • Translate complex modeling outputs into clear, actionable insights for internal teams and external partners. • Own projects end-to-end: problem framing, data exploration, modeling, deployment, and post-launch evaluation. • Help define best practices for experimentation, model evaluation, and data quality across the team.